Invited Talks

A Holistic Approach to Automatic Deep Understanding of Technical Documents

by Dr. Nikolaos Bourbakis, IEEE Fellow
Director, CART-WSU, BAIF

ABSTRACT

Most of the technical documents are composed by several modalities, like diagrams, tables, formulas, functions, algorithms, graphics, pictures and natural language text. Each of these modalities and their associations significantly contribute to the overall deep understanding of the technical document and the knowledge represented in it. Here, for us all these modalities, except NL text, are considered as “images”. Thus, each technical document mainly is composed by NL text sentences and “images”. Thus, in this talk we present a holistic approach, where all these modalities can be expressed into the same two modalities (natural languages text sentences and SPN graphs) for better associations and deeper understanding of a technical document. This deeper understanding will come from two different novel contributions.
The first unique contribution will be an enrichment of the NL text part with additional NL text sentences extracted from the “images” of the technical document. The second unique contribution will come from the SPN models of these “images” that enrich the main block diagram’s functionality by generating a simulator for the system described in that technical document.

Graph degeneracy and applications to social networks and text mining

Graph degeneracy is a popular method to approximate the densest subgraph in almost linear complexity time. In our research work we extended this method to weighted and directed graphs and capitalizing on them to investigate its potential in different graph and text mining cases. One of the cases is k-core based community evaluation – specifically metrics that integrates authority and collaboration – a properties not captured by the single node metrics or by the established community evaluation metrics. Based on the k-core, which essentially measure the robustness of a community under degeneracy, we extend it to weighted graphs. We further extend introduce novel metrics for evaluating the collaborative nature of directed graphs and define a novel D-core metric, extending the classic graph-theoretic notion of k-cores for undirected graphs to directed ones.. We applied the D-core approach on large real-world graphs such as Wikipedia and Aminer.org citation data and report interesting results. The D-core metric has been adopted by Aminer as part of its reported metrics – see an example here. We also investigate to issue of influence maximization in graphs using degeneracy as means to select the optimal spreaders. The results are promising and show that starting an epidemic from the densest k-truss. We also investigate thoroughly the issue of graph similarity via novel graph kernels and embedding schemes with applications to graph classification in chemo-informatics, social networks and text mining.

At the level of Text mining, we capitalize on the Graph-of Words (GoW) model, that capitalizes on a graph representation of documents and captures inherently the words’ order and distances in the document, apart from the frequency, to capture document similarity. We applied graph-of-word in various tasks such as ad-hoc Information Retrieval, Single-Document Keyword Extraction, Text Categorization and Sub-event Detection in Textual Streams (i.e. twitter) and document summarization. In all cases the graph of word approach, assisted by degeneracy at times, outperforms the state of the art base lines in all cases. We are currently investigating the potential of the GoW as input to deep learning architectures for text mining tasks.

Deep learning for collaborative control of mobile robots for area coverage

by Dr. Anthony Tzes, Professor at NYU in Abu Dhabi

ABSTRACT

Collaborative control of mobile robots demand the mutual exchange of information related to their position. This is used for the construction of the region of responsibility of each robot corresponding to its Voronoi cell . These cells are disjoint and their union comprise the overall area to be sensed. Rather than relying on gradient-search based collaborative control algorithms, a deep learning algorithm is suggested that adapts the heading and velocity of each robot while maximizing the combined sensed area. Simulation studies are provided to indicate the efficiency of the deep-learning based distributed controller.

Time-aware Multi-agent Symbiosis

by Prof. Panos Trahanias

ABSTRACT

Contemporary research in human-machine symbiosis has mainly concentrated on enhancing relevant sensory, perceptual and motor capacities, assuming short-term and nearly momentary interaction between the two ends. Still, human-machine confluence encompasses an inherent temporal dimension that is typically overlooked by current research endeavors. In the current keynote we present a novel approach aiming at bridging the fundamental gap of time perception in multi-agent interaction by shifting the focus of human-machine coexistence on the temporal and long-lasting aspects of symbiosis. We put forward the concept of “entimed” cognition postulating that intelligent thinking is largely determined by the temporal properties of the cognitive processes and real-world phenomena. According to entimed cognition, artificial partners acquire capacities that are inherently existent in humans, such as being able to dynamically allocate cognitive resources, to shift attention in the past and future, to recall experiences, to make associations between asynchronous events and more. We examine the role of time in multi-agent systems, considering particularly the case of symbiotic human-robot interaction (sHRI). The proposed research computationally models time perception and replicates relevant processes in-silico, adopting a rigorous research approach that involves modeling, integration on actual robotic platforms and real-world experiments.